Section-Wise Similarities for Classification of Subjective-Data on Time Series

  • Isaac Martín de Diego
  • Oscar S. Siordia
  • Cristina Conde
  • Enrique Cabello
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7042)


The aim of this paper is to present a novelty methodology to develop similarity measures for classification of time series. First, a linear segmentation algorithm to obtain a section-wise representation of the series is presented. Then, two similarity measures are defined from the differences between the behavior of the series and the level of the series, respectively. The method is applied to subjective-data on time series generated through the evaluations of the driving risk from a group of traffic safety experts. These series are classified using the proposed similarities as kernels for the training of a Support Vector Machine. The results are compared with other classifiers using our similarities, their linear combination and the raw data. The proposed methodology has been successfully evaluated on several databases.


Similarity Kernel Method Classification Time Series Data Segmentation 


  1. 1.
    Zhang, H., Schreiner, C., Zhang, K., Torkkola, K.: Naturalistic use of cell phones in driving and context-based user assistance. In: Proc. of the 9th Int. Conf. on Human Computer Interaction with Mobile Devices and Services, pp. 273–276 (2007)Google Scholar
  2. 2.
    Brazalez, A., et al.: CABINTEC: Cabina inteligente para el transporte por carretera. In: Proc. of the Congreso Español de Sistemas Inteligentes de Transporte (2008)Google Scholar
  3. 3.
    Tamil, E.M., et al.: A review on feature extraction & classification techniques for biosignal processing (Part I: Electrocardiogram). In: Proc. of the 4th Kuala Lumpur Int. Conference on Biomedical Engineering 2008, vol. 21, pp. 117–121 (2008)Google Scholar
  4. 4.
    Keogh, E., Pazzani, M.: An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. In: KDD, pp. 239–243 (1998)Google Scholar
  5. 5.
    Siordia, O.S., Martín, I., Conde, C., Reyes, G., Cabello, E.: Driving risk classification based on experts evaluation. In: Proceedings of the 2010 IEEE Intelligent Vehicles Symposium (IV 2010), pp. 1098–1103 (2010)Google Scholar
  6. 6.
    Randall, C., et al.: A comparison of the verbal rating scale and the visual analog scale for pain assessment. Technical Report 1, Int. Journal of Anesthesiology (2004)Google Scholar
  7. 7.
    Keogh, E., Chu, S., Hart, D., Pazzani, M.: Segmenting time series: A survey and novel approach. In: Data Mining in Time Series Databases, pp. 1–22 (1993)Google Scholar
  8. 8.
    Lachaud, J.-O., Vialard, A., de Vieilleville, F.: Analysis and comparative evaluation of discrete tangent estimators. In: Andrès, É., Damiand, G., Lienhardt, P. (eds.) DGCI 2005. LNCS, vol. 3429, pp. 240–251. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Martín, I., Muñoz, A., Moguerza, J.: Methods for the combination of kernel matrices within a support vector framework. Mach. Learn. 78, 137–174 (2010)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Keogh, E., Xi, X., Wei, L., Ratanamahatana, A.: The ucr time series classification/clustering (2006),
  11. 11.
    Ramsay, J., Silverman, B.: Functional Data Analysis. Springer Series in Statistics, Secaucus, NJ, USA (2005)Google Scholar
  12. 12.
    Ferraty, F., Vieu, P.: Nonparametric Functional Data Analysis: Theory and Practice. Springer Series in Statistics (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Isaac Martín de Diego
    • 1
  • Oscar S. Siordia
    • 1
  • Cristina Conde
    • 1
  • Enrique Cabello
    • 1
  1. 1.Face Recognition and Artificial Vision GroupUniversidad Rey Juan CarlosMóstolesEspaña

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